Traditional effectiveness of motion-based video segmentation systems depends on the quality of the motion analysis, which is performed to estimate the motion models between a pair of consecutive frames. That is, a process of determining the corresponding relationship between the locations of a scene point in two consecutive frames. However, in real imagery, it is difficult to determine the multiple motion models because the different objects in a scene have different motions. Traditional, optical flow techniques usually do not work well because the intervals between image acquisitions are not small enough in many applications.
Although estimation techniques based on texture point correspondence do not fail in such applications, it is still difficult to extract multiple global motion models due to the mismatches in point correspondences. These mismatches originate from the image noise, as well as the matching uncertainty, commonly referred to as the “aperture problem.”
Variations of Hough transform (HT) have been used to solve the problem of video motion analysis using four-dimensional voting arrays for video motion analysis, under the same conventional principle of using two-dimensional voting arrays for straight line detection. Along with the problem of being insensitive to the imperfect data or noise, using conventional implementations of Hough transform comes at the cost of storage (due in part to the size of the voting arrays) and extensive computation. In addition, these memory and computational consumption problems drastically increase if the number of model parameters increase as is the case of motion analysis.